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Challenges and opportunities in high-dimensional choice data analyses |
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| Authors: | Prasad Naik Michel Wedel Lynd Bacon Anand Bodapati Eric Bradlow Wagner Kamakura Jeffrey Kreulen Peter Lenk David M. Madigan Alan Montgomery |
| Affiliation: | 1. University of California, Davis, Davis, CA, USA 2. University of Maryland, College Park, MD, USA 3. Polimetrix Inc., Palo Alto, CA, USA 4. University of California, Los Angeles, Los Angeles, CA, USA 5. University of Pennsylvania, Philadelphia, PA, USA 6. Duke University, Durham, NC, USA 7. IBM Almaden Research Center, San Jose, CA, USA 8. University of Michigan, Ann Arbor, MI, USA 9. Columbia University, New York, NY, USA 10. Carnegie Mellon University, Pittsburgh, PA, USA |
| Abstract: | Modern businesses routinely capture data on millions of observations across subjects, brand SKUs, time periods, predictor variables, and store locations, thereby generating massive high-dimensional datasets. For example, Netflix has choice data on billions of movies selected, user ratings, and geodemographic characteristics. Similar datasets emerge in retailing with potential use of RFIDs, online auctions (e.g., eBay), social networking sites (e.g., mySpace), product reviews (e.g., ePinion), customer relationship marketing, internet commerce, and mobile marketing. We envision massive databases as four-way VAST matrix arrays of Variables?×?Alternatives?×?Subjects?×?Time where at least one dimension is very large. Predictive choice modeling of such massive databases poses novel computational and modeling issues, and the negligence of academic research to address them will result in a disconnect from the marketing practice and an impoverishment of marketing theory. To address these issues, we discuss and identify the challenges and opportunities for both practicing and academic marketers. Thus, we offer an impetus for advancing research in this nascent area and fostering collaboration across scientific disciplines to improve the practice of marketing in information-rich environment. |
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